Information processing device, information processing system, and computer program product

ABSTRACT

An information processing device includes one or more hardware processors. The hardware processors acquire a causal relationship included in a target document that is a specific document from causal relationship management information in which one or a plurality of causal relationships are registered, which are extracted from one or a plurality of documents and each which includes a set of a first element and a second element having a relationship; acquire a similar expression of the causal relationship included in the target document based on feature management information in which features of a plurality of words included in one or a plurality of documents are registered; and acquire a generalized expression of the causal relationship included in the target document based on the causal relationship included in the target document and the similar expression.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2020-007504, filed on Jan. 21, 2020; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an informationprocessing device, an information processing system, and a computerprogram product.

BACKGROUND

There is known a system for searching for a document including a searchcharacter string or a similar character string similar to the searchcharacter string from a plurality of documents. For example, a sentenceincluding important words written in a previously created important worddictionary is extracted from sentences to be searched, and the extractedsentence is converted using a generalized dictionary. Then, phraseshaving a causal relationship included in the converted sentence areextracted, and a causal relationship network in which the phrases areconnected is generated. Then, there is disclosed a system for searchingfor a case similar to an input text by matching a causal relationshipincluded in the input text and a causal network.

However, conventionally, in order to obtain a causal relationship of ageneralized expression, it is necessary to prepare an important worddictionary and a generalized dictionary in advance, and the maintenanceof the dictionaries is necessary. In addition, in order to obtain ageneralized expression of the causal relationship included in aplurality of documents in different categories, it is necessary toperform conversion in consideration of the meaning of the causalrelationship, not simple conversion. Therefore, in the conventionaltechnology, it may be difficult to improve a conversion efficiency ofthe causal relationship included in a target document into thegeneralized expression.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an information processing systemaccording to an embodiment;

FIG. 2 is a block diagram illustrating a functional configuration ofeach of an information processing device and a terminal device accordingto the embodiment;

FIG. 3 is a schematic diagram of a document according to the embodiment;

FIG. 4 is a schematic diagram illustrating a data configuration ofdocument management information according to the embodiment;

FIG. 5 is a schematic diagram illustrating a data configuration ofcausal relationship management information according to the embodiment;

FIG. 6 is a schematic diagram illustrating a data configuration offeature management information according to the embodiment;

FIG. 7 is a schematic diagram of a display screen according to theembodiment;

FIG. 8 is a schematic diagram illustrating a data configuration ofco-occurrence management information according to the embodiment;

FIG. 9 is a schematic diagram of a display screen according to theembodiment;

FIG. 10 is a schematic diagram of a display screen according to theembodiment;

FIG. 11 is a schematic diagram of a display screen according to theembodiment;

FIG. 12 is a schematic diagram of a display screen according to theembodiment;

FIG. 13 is a schematic diagram of a display screen according to theembodiment;

FIG. 14 is a schematic diagram of a data configuration of generalizedmanagement information according to the embodiment;

FIG. 15 is a flowchart illustrating a flow of information processingexecuted by the information processing device according to theembodiment;

FIG. 16 is a block diagram illustrating a functional configuration of aninformation processing device according to the embodiment; and

FIG. 17 is a hardware configuration diagram of the informationprocessing device and the terminal device according to the embodiment.

DETAILED DESCRIPTION

According to an embodiment, an information processing device includesone or more hardware processors. The hardware processors acquire acausal relationship included in a target document that is a specificdocument from causal relationship management information in which one ora plurality of causal relationships are registered, the one or theplurality of causal relationships being extracted from one or aplurality of documents and each including a set of a first element and asecond element having a relationship; acquire a similar expression ofthe causal relationship included in the target document, based onfeature management information in which features of a plurality of wordsincluded in the one or the plurality of document are registered; andacquire a generalized expression of the causal relationship included inthe target document, based on the causal relationship included in thetarget document and the similar expression.

An information processing device, an information processing system, anda computer program product according to the present embodiment will bedescribed below in detail with reference to the accompanying drawings.

FIG. 1 is a schematic diagram illustrating an example of an informationprocessing system 1 of the present embodiment.

The information processing system 1 includes an information processingdevice 10 and a terminal device 30. The information processing device 10and the terminal device 30 are communicably connected via a wireless orwired communication network such as a network N.

The information processing device 10 is a dedicated or general-purposecomputer. In the present embodiment, the information processing device10 executes processing described later on a document. Details of thedocument and the processing will be described later.

The terminal device 30 is a computer operated by a user. The terminaldevice 30 is, for example, a computer, a mobile terminal, a smartphone,or the like, but is not limited to thereto.

Note that FIG. 1 illustrates, as an example, a configuration in whichthe information processing system 1 includes one information processingdevice 10 and one terminal device 30. However, the informationprocessing system 1 may be configured to include one or a plurality ofinformation processing devices 10 and one or a plurality of terminaldevices 30.

FIG. 2 is a block diagram illustrating an example of a functionalconfiguration of each of the information processing device 10 and theterminal device 30 of the present embodiment.

In the present embodiment, the terminal device 30 is a device operatedby a user when communicating with the information processing device 10.

The terminal device 30 includes a storage unit 32, a user interface (UI)unit 34, a communication unit 36, and a main control unit 38. Thestorage unit 32, the UI unit 34, the communication unit 36, and the maincontrol unit 38 are connected via a bus 39 so that data or signals canbe exchanged.

The storage unit 32 stores various data. The storage unit 32 is, forexample, a random access memory (RAM), a semiconductor memory elementsuch as a flash memory, a hard disk, an optical disk, or the like. Notethat the storage unit 32 may be a storage medium. Specifically, thestorage medium may be one in which programs and various types ofinformation are downloaded and stored or temporarily stored via a localarea network (LAN), the Internet, or the like. In addition, the storageunit 32 may be configured by a plurality of storage media.

The communication unit 36 communicates with the information processingdevice 10 via the network N. The UI unit 34 has a function of receivingan operation input by the user and a function of outputting varioustypes of information.

For example, the UI unit 34 includes a display and an input unit. Thedisplay displays various types of information. The display is, forexample, a known organic electro-luminescence (EL) display, liquidcrystal display (LCD), projection device, or the like. The input unitreceives various instructions from the user. The input unit is, forexample, a keyboard, a mouse, a touch panel, a microphone, or the like.Note that the UI unit 34 may be configured with a touch panel having aninput mechanism and an output mechanism. In addition, the UI unit 34 mayfurther include a speaker that outputs a sound. The main control unit 38controls each unit of an electronically controllable device provided inthe terminal device 30.

Next, the information processing device 10 will be described.

The information processing device 10 includes a storage unit 12, a UIunit 14, a communication unit 16, and a control unit 18. The storageunit 12, the UI unit 14, the communication unit 16, and the control unit18 are connected via a bus 19 so that data or signals can be exchanged.

At least one of the storage unit 12 and the UI unit 14 may be connectedto the control unit 18 via the network N. That is, at least one of thestorage unit 12 and the UI unit 14 may be provided in an external deviceconnected to the information processing device 10 via the network N. Inaddition, at least one of the functional units described later includedin the control unit 18 may be provided in the external device. Theexternal device is, for example, an external server.

The storage unit 12 stores various data. The storage unit 12 is, forexample, a RAM, a semiconductor memory device such as a flash memory, ahard disk, an optical disk, or the like. The storage unit 12 may be astorage device provided outside the information processing device 10. Inaddition, the storage unit 12 may be a storage medium. Specifically, thestorage medium may be one in which programs and various types ofinformation are downloaded and stored or temporarily stored via a localarea network (LAN), the Internet, or the like. The storage unit 12 maybe configured by a plurality of storage media.

In the present embodiment, the storage unit 12 stores documentmanagement information 12A, causal relationship management information12B, feature management information 12C, co-occurrence managementinformation 12D, and generalized management information 12E. Details ofthe information will be described later.

The UI unit 14 has a function of receiving an operation input by theuser and a function of outputting various types of information. Forexample, the UI unit 14 includes a display and an input unit. An exampleof the display and the input unit is the same as the UI unit 34. Thecommunication unit 16 communicates with the terminal device 30 via thenetwork N.

The control unit 18 includes a learning module 20 and a conversionsupport processing module 22. The learning module 20 includes a causalrelationship learning module 20A and a feature learning module 20B. Theconversion support processing module 22 includes a target documentacquisition module 22A, a causal relationship acquisition module 22B, asimilar expression acquisition module 22C, and an acquisition module22D. The similar expression acquisition module 22C includes a prioritydetermination module 22E. The acquisition module 22D includes aco-occurrence word acquisition module 22F, a display control module 22G,a reception module 22H, and a registration module 22I.

At least one of the learning module 20, the causal relationship learningmodule 20A, the feature learning module 20B, the conversion supportprocessing module 22, the target document acquisition module 22A, thecausal relationship acquisition module 22B, the similar expressionacquisition module 22C, the acquisition module 22D, the prioritydetermination module 22E, the co-occurrence word acquisition module 22F,the display control module 22G, the reception module 22H, and theregistration module 22I is realized by, for example, one or moreprocessors. For example, each of the modules may be realized by causinga processor such as a central processing unit (CPU) to execute aprogram, that is, by software. Each of the modules may be realized by aprocessor such as a dedicated integrated circuit (IC), that is,hardware. Each of the modules may be realized by using software andhardware in combination. When using a plurality of processors, eachprocessor may realize one of the modules or two or more of the modules.

The learning module 20 is a functional unit that executes machinelearning based on a document.

The document is data that includes a plurality of words. The word is amorpheme. The morpheme is the smallest unit of a meaningful expressionelement. The document is, for example, a report or case data ofmeetings, experiments, various phenomena, and various projects.

Specifically, for example, the document is case data of troubles thatoccurred in the past in design, manufacturing, and operation invehicles, aircraft, various products, various projects, and the like.Design includes, for example, product design of thermal power, hydraulicpower, and nuclear power plants, building air conditioning managementsystems, water and sewer management systems, and the like. In addition,the document may be a document of a rule kind of a technical fieldrelated to the product or the like.

Experimental data, which is an example of the report, includesexperimental conditions and data indicating experimental results. Thecase data may include a title, a phenomenon, a cause of the phenomenon,a countermeasure against the cause, a corresponding product name, andthe like. In addition, the report may include data indicating a problemand an effect on the problem. In addition, the case data may includedata indicating a cause of a specific phenomenon and a result of thecause, and the like. In addition, the document may include datatime-serially or conceptually indicating a higher order phenomenon and alower order phenomenon having a relationship related to the higher orderphenomenon.

FIG. 3 is a schematic diagram illustrating an example of a document 40.FIG. 3 illustrates case data of a trouble as an example of the document40. For example, the document 40 includes words regarding a title of thedocument 40, a phenomenon that is the content of the trouble, a cause ofthe phenomenon, a countermeasure against the phenomenon, a product namein which the phenomenon occurs, and the like. Note that the contentincluded in the document 40 is not limited to the content illustrated inFIG. 3. In addition, a format of the document 40 is not limited to theformat illustrated in FIG. 3.

Returning to FIG. 2, the description will be continued. In the presentembodiment, a plurality of documents 40 are registered in the documentmanagement information 12A in advance.

FIG. 4 is a schematic diagram illustrating an example of a dataconfiguration of the document management information 12A. The documentmanagement information 12A is a database in which a document ID, acategory, and the document 40 are associated with each other. A dataformat of the document management information 12A is not limited to thedatabase.

The document ID is identification information for identifying thecorresponding document 40. The category is a name of each group when theplurality of documents 40 are classified into a plurality of groupsaccording to a predetermined classification condition. The category is,for example, a product, a field, an industry, a management department, adate, a time period, or the like.

A category of the corresponding document 40 is registered in thedocument management information 12A. FIG. 4 illustrates an example of acase in which the category is the product name. That is, FIG. 4illustrates, as an example, a form in which the category indicatingwhich product the document 40 belongs to is registered in the documentmanagement information 12A in advance. Note that, as described above,the category is not limited to the product name.

In the present embodiment, a form in which documents 40A to 40C areregistered in the document management information 12A in advance as theplurality of documents 40 is described as an example. Note that it issufficient that at least one document 40 is registered in the documentmanagement information 12A in advance, and the number of registereddocuments 40 is not limited to three.

Returning to FIG. 2, the description will be continued. In the presentembodiment, the learning module 20 uses the document managementinformation 12A to execute machine learning. The learning module 20includes a causal relationship learning module 20A and a featurelearning module 20B.

The causal relationship learning module 20A extracts a causalrelationship included in the document 40 registered in the documentmanagement information 12A based on the document management information12A and registers the extracted causal relationship in the causalrelationship management information 12B.

The causal relationship is data configured by a set of a first elementand a second element having a relationship. The set of the first elementand the second element is, for example, a cause and a result, acondition and a result or a conclusion, a problem and an effect, ahigher element and a lower element, and the like. The higher element isan element that represents a higher phenomenon in a time series orconceptually. The lower element is an element that represents a lowerphenomenon having a relationship with the higher element.

The first element and the second element are represented by a group ofone or a plurality of words.

In the present embodiment, the case where the set of the first elementand the second element having the relationship is a set of the cause andthe result will be described as an example. Note that as describedabove, the set of the first element and the second element is notlimited to the set of the cause and the result.

The causal relationship learning module 20A extracts a causalrelationship included in each of the plurality of documents 40 from theplurality of documents 40 registered in the document managementinformation 12A. The causal relationship learning module 20A extracts aplurality of causal relationships including the set of the cause and theresult from each of the plurality of documents 40 (document 40A todocument 40C) using a known technique.

For example, in order to extract the causal relationship by the causalrelationship learning module 20A, a machine learning method illustratedin Document 1 or a pattern matching illustrated in Document 2 may beused.

-   -   Document 1: Kruengkrai et al., Improving Event Causality        Recognition With Multiple Background Knowledge Sources Using        Multi-Column Convolutional Neural Networks, AAAI′17 Proceedings        of the Thirty-First AAAI Conference on Artificial Intelligence,        pp. 3466-3473, 2017    -   Document 2: Sakaji et al., Extraction of cause/result        expressions from PDF of financial results, IEICE Transactions D,        Vol. J98-D, NO.5, pp. 811-822, 2015

The causal relationship learning module 20A registers the causalrelationship extracted from each of the plurality of documents 40(document 40A to document 40B) in the causal relationship managementinformation 12B. Therefore, the causal relationship managementinformation 12B is in a state in which one or a plurality of causalrelationships extracted from each of one or a plurality of documents 40and configured by the set of the first element and the second elementhaving the relationship are registered.

FIG. 5 is a schematic diagram illustrating an example of a dataconfiguration of the causal relationship management information 12B. Thecausal relationship management information 12B is a database in which acausal relationship ID, a document ID, a category, a causal relationship42, and a generalized ID are associated with each other. A data formatof the causal relationship management information 12B is not limited tothe database.

The causal relationship ID is identification information of thecorresponding causal relationship 42. The causal relationship 42includes a first element and a second element. As described above, inthe present embodiment, the case where the set of the first element andthe second element is the set of the cause and the result will bedescribed as an example. Therefore, in the following, the case where thecausal relationship 42 is data of a set of a cause 42A and a result 42Bwill be described as an example. The generalized ID is identificationinformation of a generalized expression of the causal relationship 42.Details of the generalized expression will be described later.

Returning to FIG. 2, the description will be continued. The featurelearning module 20B learns a feature of each of a plurality of wordsincluded in each of the one or the plurality of documents 40 andregisters the feature in the feature management information 12C.

FIG. 6 is a schematic diagram illustrating an example of a dataconfiguration of the feature management information 12C. The featuremanagement information 12C is a database that associates words,features, and the number included in each category. The words registeredin the feature management information 12C are words included in each ofthe plurality of documents 40 registered in the document managementinformation 12A.

The feature is data indicating the feature of the corresponding word.The feature is represented by, for example, a vector expression acquiredfrom the relationship with preceding and succeeding words for each ofthe words included in the document 40.

The feature management information 12C uses each of the words includedin the document 40 as learning data and learns a vector expression of aphrase by an extension learning method in which the word embeddingtechnique is extended to n-gram (n consecutive words, hereinafter, alsoreferred to as a phrase). The word embedding technique regards asequence of words arranged before and after a learning target word asthe context of the learning target word. In the word embeddingtechnique, a neural network is used to learn the vector expression ofthe word so that words that are semantically similar are arranged toclose to each other in a vector space. The vector expression of the wordis sometimes referred to as an embedded vector.

Through such learning, the feature management information 12C learns thevector expression of each of the words included in the document 40. Notethat as the extension learning method, for example, a method illustratedin Document 3 below may be used.

-   -   Document 3: Zhao et al., Ngram2vec: Learning Improved Word        Representations from Ngram Co-occurrence Statistics, Proceedings        of the 2017 Conference on Empirical Methods in Natural Language        Processing, pp. 244-253, 2017

The number included in each category is information indicating how manycorresponding words are included in the document 40 belonging to each ofthe plurality of categories. As an example, FIG. 6 illustrates numericalvalues indicating how many corresponding words are included in each offour categories. Specifically, [125,12,0,0] means that 125 words of“shaking” are included in the document 40 belonging to a category“Product A”, 12 words of “shaking” are included in the document 40belonging to a category “Product B”, and 0 word of “shaking” is includedin the document 40 belonging to categories “Product C” and “Product D”.

The feature learning module 20B acquires the number included in eachgroup of the documents 40 belonging to each category by using a knownanalysis method for each word included in each of the plurality ofdocuments 40. Then, the feature learning module 20B may register theacquisition result in the feature management information 12C as thenumber included in each category of each of the words.

Returning to FIG. 2, the description will be continued. Every time thelearning module 20 acquires a new document 40 via the external device orthe UI unit 14, the learning module 20 may register the acquireddocument 40 in the document management information 12A. In addition,when the document 40 is registered in the document managementinformation 12A, the learning module 20 may specify a category of thedocument 40 by a known method, assign a document ID, and register thedocument 40 in the document management information 12A. For example, thelearning module 20 may search for a word included in the document 40,which is an identification target of the category, and use the searchresult to specify the category. Note that the document managementinformation 12A may use a category input by the user when registeringthe document 40 in the document management information 12A as thecategory of the document 40.

In addition, in the learning module 20, each time the new document 40 isregistered in the document management information 12A, each of thecausal relationship learning module 20A and the feature learning module20B performs the learning processing. Therefore, the learning module 20can register the learning result using all the documents 40 registeredin the document management information 12A in the causal relationshipmanagement information 12B and the feature management information 12C.

Next, the conversion support processing module 22 will be described.

The conversion support processing module 22 executes processing forsupporting a conversion of the causal relationship 42 included in thedocument 40 into a generalized expression.

The conversion into the generalized expression means converting at leastone of the cause 42A and the result 42B included in the causalrelationship 42 to a generalized expression that is not limited to aspecific expression within a category. That is, the generalizedexpression means a generalized expression that is not limited to thespecific expression within the category.

The conversion support processing module 22 includes a target documentacquisition module 22A, a causal relationship acquisition module 22B, asimilar expression acquisition module 22C, and an acquisition module22D.

The target document acquisition module 22A acquires a target document.The target document is a document 40 including a causal relationship 42of a conversion target into the generalized expression. In other words,the target document is one specific document 40 among the plurality ofdocuments 40 registered in the document management information 12A.

The target document acquisition module 22A acquires, for example, thedocument 40 selected by the user among the plurality of documents 40registered in the document management information 12A as the targetdocument.

In this case, the target document acquisition module 22A displays, forexample, a display screen for selecting the target document, andcontrols to accept an operation from the user.

For example, the target document acquisition module 22A displays thedisplay screen on the UI unit 34 of the terminal device 30 operated bythe user. Note that the conversion support function executed by theconversion support processing module 22 of the present embodiment isprovided in advance to the terminal device 30 as a Web application, forexample. For example, the user uses the conversion support function viaa Web browser installed in the terminal device 30.

FIG. 7 is a schematic diagram illustrating an example of a displayscreen 51 displayed on the UI unit 34. The display screen 51 is anexample of a display screen 50. The display screen 50 is an example ofan interface screen displayed on a Web browser screen of the UI unit 34when the terminal device 30 accesses the conversion support function viathe Web browser.

The display screen 51 includes a search query input field 50A and a listdisplay area 50B. The search query input field 50A is a search queryinput field. The list display area 50B is a display area of a list ofthe searched documents 40.

For example, the user inputs a search query of the document 40 to besearched to the search query input field 50A. Then, the main controlunit 38 of the terminal device 30 transmits the search query to theinformation processing device 10 via the communication unit 36. Thetarget document acquisition module 22A of the information processingdevice 10 searches for the document 40 corresponding to the receivedsearch query from the document management information 12A, and transmitsthe searched document to the terminal device 30 via the communicationunit 16. Then, the main control unit 38 of the terminal device 30displays the list of the documents 40 received from the informationprocessing device 10 in the list display area 50B of the display screen51.

Therefore, the list of the documents 40 is displayed on the displayscreen 51. The user selects a document 40 to be converted into thegeneralized expression from the plurality of documents 40 displayed inthe list display area 50B. The terminal device 30 transmits the document40 or the document ID of the document 40 for which the selection isaccepted to the information processing device 10 via the communicationunit 36. The target document acquisition module 22A acquires thedocument 40 received from the terminal device 30 or the document 40corresponding to the document ID as a target document.

In the present embodiment, an example in which the document 40Aillustrated in FIG. 3 is acquired as the target document 41 will bedescribed as an example.

Returning to FIG. 2, the description will be continued. The causalrelationship acquisition module 22B acquires the causal relationship 42included in the target document 41 from the causal relationshipmanagement information 12B. The causal relationship acquisition module22B acquires the causal relationship 42 by reading one or a plurality ofcausal relationships 42 corresponding to the document ID of the targetdocument 41 from the causal relationship management information 12B.

The causal relationship acquisition module 22B may acquire the causalrelationship 42 selected by the user from the plurality of causalrelationships 42 acquired from the causal relationship managementinformation 12B.

In this case, the causal relationship acquisition module 22B displays alist of the plurality of causal relationships 42 included in the targetdocument 41 on the UI unit 34 of the terminal device 30. The userselects a desired causal relationship 42 with reference to the UI unit34. The main control unit 38 of the terminal device 30 transmits thecausal relationship 42 or the causal relationship ID of the causalrelationship 42 received from the UI unit 34 to the informationprocessing device 10 via the communication unit 36. Then, the causalrelationship acquisition module 22B of the information processing device10 may acquire the causal relationship 42 selected by the user byreceiving the causal relationship 42 or the causal relationship ID fromthe terminal device 30. Note that when the causal relationship ID isreceived, the causal relationship acquisition module 22B may acquire thecausal relationship 42 by reading the causal relationship 42corresponding to the causal relationship ID from the causal relationshipmanagement information 12B.

Next, the similar expression acquisition module 22C will be described.The similar expression acquisition module 22C acquires a similarexpression of the causal relationship 42 included in the target document41 based on the feature management information 12C.

The similar expression is an expression similar to at least one of thecause 42A and the result 42B included in the causal relationship 42. Thesimilar expression acquisition module 22C acquires a group of wordshaving a feature similar to each of the words that constitute the causalrelationship 42 in the target document 41 among the words registered inthe feature management information 12C as the similar expression of thecausal relationship 42. The similar expression acquisition module 22Cspecifies words that constitute each of the causal relationships 42(cause 42A and result 42B) by morphologically analyzing the causalrelationships 42 included in the target document 41. Then, the similarexpression acquisition module 22C specifies the specified words, thatis, a group of words having a feature similar to a phrase that is amorpheme or a continuous morpheme string from the feature managementinformation 12C, and acquires the specified words as the similarexpression 43.

This will be described with reference to FIG. 6. For example, thedescription will be made by assuming that the similar expressionacquisition module 22C acquires a similar expression of “shaking”, whichis a word included in the cause 42A, as a word that constitutes thecausal relationship 42 in the target document 41. In this case, thesimilar expression acquisition module 22C specifies, for example,“vibration” as a word similar to the vector expression that is thefeature of the word “shaking” among the words registered in the featuremanagement information 12C. The similar expression acquisition module22C may acquire, as the similar expression, another word having asimilarity score with the target word for which the similar expressionis acquired that is a threshold value or more.

Specifically, the similar expression acquisition module 22C calculates asimilarity score with the vector expression that is the feature of theword “shaking”, for example, by a cosine distance, for each of the wordsregistered in the causal relationship management information 12B. Then,the similar expression acquisition module 22C acquires, as the similarexpressions of the word “shaking”, words having similarity scoresexceeding a predetermined threshold value in the descending order of thesimilarity scores. Note that the threshold value is not limited to thesimilarity score. For example, the threshold value may be set to themaximum number of words acquired as the similar expression.

Then, the similar expression acquisition module 22C acquires the similarexpression for each word included in each of the cause 42A and theresult 42B included in the causal relationship 42 in the same manner asthat described above. Through such processing, the similar expressionacquisition module 22C acquires the similar expression of the cause 42Aand the similar expression of the result 42B as the similar expressionof the causal relationship 42.

Note that the similar expression acquisition module 22C may acquire aword having a high priority among the similar words as the similarexpression. In this case, the similar expression acquisition module 22Cmay be configured to include a priority determination module 22E (seeFIG. 2).

The priority determination module 22E determines a priority of each ofthe words registered in the feature management information 12C. Thepriority determination module 22E may determine the priority of each ofthe words using a predetermined determination condition.

For example, the priority determination module 22E specifies thecategory to which the target document 41 belongs. Then, the prioritydetermination module 22E determines a higher priority as the more wordsare included in other categories other than the specified category. Thiswill be described with reference to FIG. 6. For example, it is assumedthat the category of the document 40A acquired as the target document 41is “product A” (see FIG. 4). In this case, the priority determinationmodule 22E determines the higher priority as the more words are includedin at least one of “Product B”, “Product C”, and “Product D” that arecategories other than “Product A”, for each of the words registered inthe feature management information 12C.

Then, the similar expression acquisition module 22C determines a groupof words having the determined priority higher than a first priorityamong other words whose features are similar to the words thatconstitute the causal relationship 42 in the target document 41, as thesimilar expression of the causal relationship 42. The first priority maybe set in advance.

For example, it is assumed that the priority determination module 22Ecalculates (total of numbers included in categories other than categoryof target word)/(number included in category of target word) as thepriority in a priority calculation method. In this case, the similarexpression acquisition module 22C may set the first priority to “1.0”and acquire a group of words higher than the first priority as thesimilar expression of the causal relationship 42, for example.

In addition, the similar expression acquisition module 22C may acquire,as the similar expression of the causal relationship 42, a predeterminednumber of words from the higher order in the order of the determinedpriority, among other words having similar features.

The similar expression acquisition module 22C may acquire the similarexpression by targeting only a word having a predetermined part ofspeech among the parts of speech of the words included in the causalrelationship 42 of the target document 41. For example, the similarexpression acquisition module 22C specifies the part of speech of theword included in the causal relationship 42 of the target document 41.The part of speech is, for example, a verb, an adjective, an adjectiveverb, a noun, an adverb, an adnominal, a conjunction, an interjection,an auxiliary verb, a particle, or the like. For example, the similarexpression acquisition module 22C may select only a word whose part ofspeech is a noun from among the words included in the causalrelationship 42, and then, the similar expression acquisition module 22Cmay acquire, as the similar expression, a group of words whose part ofspeech is the noun among words having similar features to the selectedword.

For each of the causal relationships 42 included in the target document41, the similar expression acquisition module 22C executes theprocessing described above for each word that constitutes each of thecause 42A and the result 42B that constitute the causal relationship 42.Through the processing, the similar expression acquisition module 22Cacquires the similar expressions of the cause 42A and the result 42B foreach of the one or the plurality of causal relationships 42 included inthe target document 41.

Next, the acquisition module 22D will be described. The acquisitionmodule 22D acquires a generalized expression of the causal relationship42 included in the target document 41 based on the causal relationship42 included in the target document 41 and the similar expression. Theacquisition module 22D may acquire the generalized expression of thecausal relationship 42 included in the target document 41 based on thecausal relationship 42 included in the target document 41, the similarexpression, and the co-occurrence word.

In the present embodiment, the acquisition module 22D includes aco-occurrence word acquisition module 22F, a display control module 22G,a reception module 22H, and a registration module 22I.

The co-occurrence word acquisition module 22F acquires a co-occurrenceword of the words that constitute the causal relationship 42 included inthe target document 41. The co-occurrence word is another word that hasa high probability of being used together with the word.

In the present embodiment, the co-occurrence word acquisition module 22Facquires the co-occurrence word using the co-occurrence managementinformation 12D.

FIG. 8 is a schematic diagram illustrating an example of a dataconfiguration of the co-occurrence management information 12D. Theco-occurrence management information 12D is a database in which wordsare associated with one or a plurality of co-occurrence words for thewords. The data configuration of the co-occurrence managementinformation 12D is not limited to the database.

The co-occurrence management information 12D may be stored in thestorage unit 12 in advance. For example, the information processingdevice 10 may acquire the co-occurrence management information 12D fromthe external device via the network N and store the co-occurrencemanagement information 12D in the storage unit 12. In addition, forexample, the information processing device 10 extracts a plurality ofwords included in each of the plurality of documents 40 registered inthe document management information 12A and uses each of the pluralityof extracted words. Then, the information processing device 10 maycreate the co-occurrence management information 12D in advance by usingthe data and a known co-occurrence degree calculation method.

Returning to FIG. 2, the description will be continued. Theco-occurrence word acquisition module 22F acquires a co-occurrence wordrelated to the words that constitute the causal relationship 42 includedin the target document 41 from the co-occurrence management information12D. The co-occurrence word related to the words means a co-occurrenceword corresponding to the word and a co-occurrence word for a similarexpression of the word. For example, the co-occurrence word acquisitionmodule 22F acquires at least one of the co-occurrence word correspondingto each of the words that constitute the causal relationship 42 includedin the target document 41 and the co-occurrence word for the similarexpression of the word, from the co-occurrence management information12D.

Note that the co-occurrence word acquisition module 22F may acquire aco-occurrence word of a word selected by the user from the co-occurrencewords related to the words that constitute the causal relationship 42included in the target document 41.

Note that the acquisition module 22D is not limited to the configurationincluding the co-occurrence word acquisition module 22F. The acquisitionmodule 22D may not have the co-occurrence word acquisition module 22F.In the present embodiment, the case where the acquisition module 22Dincludes the co-occurrence word acquisition module 22F is described asan example.

The display control module 22G displays the display screen 50 includingthe causal relationship 42 included in the target document 41 and thesimilar expression on the display unit. In the present embodiment, theUI unit 34 corresponds to an example of the display unit. The UI unit 14provided in the information processing device 10 may be an example ofthe display unit.

By a display control by the display control module 22G, the displayscreen 50 including the causal relationship 42 and the similarexpression is displayed on the UI unit 34 of the terminal device 30.

FIG. 9 is a schematic diagram illustrating an example of a displayscreen 52. The display screen 52 is an example of the display screen 50.The display screen 52 includes a target document display area 60, acausal relationship display area 62, a similar expression display area63, and a generalized expression input field 64. The display screen 52may be the display screen 50 including at least the causal relationshipdisplay area 62, the similar expression display area 63, and thegeneralized expression input field 64.

The target document display area 60 is a display area of the targetdocument 41. The causal relationship display area 62 is a display areaof the causal relationship 42 included in the target document 41. FIG. 9illustrates, as an example, a form in which one causal relationship 42is displayed in the causal relationship display area 62.

The similar expression display area 63 is a display area of the similarexpression 43 acquired by the similar expression acquisition module 22C.As illustrated in FIG. 9, a similar expression 43A that is the similarexpression 43 of the cause 42A displayed in the causal relationshipdisplay area 62 and a similar expression 43B that is the similarexpression 43 of the result 42B are displayed in the similar expressiondisplay area 63.

The generalized expression input field 64 is an input field of thegeneralized expression 44 corresponding to the causal relationship 42.The generalized expression input field 64 includes an input field 64Aand an input field 64B. The input field 64A is an input field of ageneralized expression 44A of the cause 42A. The input field 64B is aninput field of a generalized expression 44B of the result 42B.

As illustrated in FIG. 9, the display screen 52 includes a causalrelationship display area 62 and a similar expression display area 63.Therefore, the user can easily check the generalized expression 44 ofthe causal relationship 42 and input the generalized expression 44 intothe generalized expression input field 64 while visually confirming thecausal relationship 42 displayed in the causal relationship display area62 and the similar expression 43 of the causal relationship 42 displayedin the similar expression display area 63.

Then, the user may operate a registration button 65 after completing theinput of the generalized expression 44 into the generalized expressioninput field 64. By the operation, the terminal device 30 transmits theinput generalized expression 44 and the causal relationship ID of thecausal relationship 42 that was the processing target when thegeneralized expression 44 was input to the information processing device10 via the communication unit 36.

Note that the causal relationship acquisition module 22B may acquire aplurality of causal relationships 42 from the target document 41. Inthis case, the display control module 22G may display the display screen50 including the plurality of causal relationships 42 and the similarexpression 43 of each of the plurality of causal relationships 42 on theUI unit 34.

FIG. 10 is a schematic diagram illustrating an example of a displayscreen 53. The display screen 53 is an example of the display screen 50.The display screen 53 includes a target document display area 60, acausal relationship display area 62, a similar expression display area63, and a generalized expression input field 64.

The display screen 53 has a plurality of causal relationship displayareas 62 (causal relationship display area 62A and causal relationshipdisplay area 62B) as the causal relationship display area 62. In thecausal relationship display area 62A, a causal relationship 420including a set of a cause 42A1 and a result 42B1 is displayed. In thecausal relationship display area 62B, a causal relationship 421including a set of a cause 42A2 and a result 42B2 is displayed. Thecausal relationship 420 and the causal relationship 421 are examples ofthe causal relationship 42.

In addition, the display screen 53 has a plurality of similar expressiondisplay areas 63 (similar expression display area 63A and similarexpression display area 63B) as the similar expression display area 63.The similar expression display area 63A is a display area of the similarexpression 43 (similar expressions 43A1 and 43B1) corresponding to thecausal relationship 420 displayed in the causal relationship displayarea 62A. The similar expression display area 63B is a display area ofthe similar expression (similar expressions 43A2 and 43B2) correspondingto the causal relationship 421 displayed in the causal relationshipdisplay area 62B.

Thus, the display screen 53 may be the display screen 50 displaying thesimilar expression 43 corresponding to each of the plurality of causalrelationships 42 included in the target document 41. In addition, theuser can associate the causal relationship with the generalizedexpression by performing an operation (a plurality of selections arepossible) such as setting the causal relationship corresponding to theinput generalized expression in a selected state through the displayscreen 53.

In addition, as described above, the acquisition module 22D of thepresent embodiment may be configured to include the co-occurrence wordacquisition module 22F. In this case, the display control module 22G maydisplay the display screen 50 including the causal relationship 42 ofthe target document 41, the similar expression 43, and the co-occurrenceword 45 on the UI unit 34.

FIG. 11 is a schematic diagram illustrating an example of a displayscreen 54. The display screen 54 is an example of the display screen 50.The display screen 54 includes a target document display area 60, acausal relationship display area 62, a similar expression display area63, a generalized expression input field 64, and a co-occurrence worddisplay area 66.

The co-occurrence word display area 66 is a display area of theco-occurrence word 45 of the words included in the causal relationship42. For example, the similar expression 43A of each of the wordsincluded in the cause 42A displayed in the causal relationship displayarea 62 is displayed in the similar expression display area 63, and theco-occurrence word 45A of the selected word among the words is displayedin the co-occurrence word display area 66. In addition, for example, thesimilar expression 43B of each of the words included in the result 42Bdisplayed in the causal relationship display area 62 is displayed in thesimilar expression display area 63, and the co-occurrence word 45B ofthe selected word among the words is displayed in the co-occurrence worddisplay area 66.

Therefore, the user can easily check the generalized expression 44 ofthe causal relationship 42 and input the generalized expression 44 intothe generalized expression input field 64 while visually confirming thecausal relationship 42 displayed in the causal relationship display area62, the similar expression 43 of the causal relationship 42 displayed inthe causal relationship display area 62, and the co-occurrence word 45displayed in the co-occurrence word display area 66.

The main control unit 38 of the terminal device 30 transmits the inputgeneralized expression 44, and the causal relationship ID of the causalrelationship 42 that was the processing target when the generalizedexpression 44 was input to the information processing device 10 via thecommunication unit 36.

Note that the display control module 22G may display a list ofcandidates for the generalized expression 44 on the display screen 50.

FIG. 12 is a schematic diagram illustrating an example of a displayscreen 55. The display screen 55 is an example of the display screen 50.The display screen 55 includes a target document display area 60, acausal relationship display area 62, a similar expression display area63, a generalized expression input field 64, a co-occurrence worddisplay area 66, and a candidate selection field 67.

The candidate selection field 67 is a display field of candidates forthe generalized expression 44 corresponding to the causal relationship42 included in the target document 41. The display control module 22Greads the generalized ID corresponding to the causal relationship ID ofthe causal relationship 42 selected as the processing target from thecausal relationship management information 12B (see FIG. 5). Then, thedisplay control module 22G may read the generalized expression 44corresponding to the read generalized ID from the generalized managementinformation 12E described later and display the generalized expression44 in the candidate selection field 67.

The display control module 22G may read the generalized IDs of othercausal relationships 42 similar to the causal relationship 42 inaddition to the generalized ID corresponding to the causal relationship42 selected as the processing target. In this case, for example, thedisplay control module 22G extracts a keyword from the causalrelationship 42 selected by the user, and uses the keyword as a searchkeyword to search the causal relationship management information 12B forthe causal relationship in which the keyword matches. Then, the displaycontrol module 22G may read a generalized ID corresponding to the causalrelationship 42 of a search result and display the generalized ID inaddition to a generalized expression candidate.

By operating a pull-down button 67A, the user may select a desiredgeneralized expression 44 from a list of generalized expressions 44displayed in the candidate selection field 67.

In this case, the main control unit 38 of the terminal device 30transmits the generalized expression 44 selected by the user and thecausal relationship ID of the causal relationship 42 that was theprocessing target when the generalized expression 44 was selected to theinformation processing device 10 via the communication unit 36.

Note that the display screen 50 may be configured to display the similarexpression 43 corresponding to the causal relationship 42 selected bythe user among the plurality of causal relationships 42 included in thetarget document 41.

FIG. 13 is a schematic diagram illustrating an example of a displayscreen 56. The display screen 56 is an example of the display screen 50.The display screen 56 includes a target document display area 60, acausal relationship display area 62, a similar expression display area63, a generalized expression input field 64, a co-occurrence worddisplay area 66, and a candidate selection field 67.

In the causal relationship display area 62 of the display screen 56, aplurality of causal relationships 42 (causal relationship 420 and causalrelationship 421) are displayed.

For example, it is assumed that the user operates the UI unit 34 toselect the causal relationship 421 from the plurality of causalrelationships 42. In this case, the main control unit 38 of the terminaldevice 30 transmits the causal relationship 420 that has received theselection or the causal relationship ID of the causal relationship 420to the information processing device 10 via the communication unit 36.The display control module 22G of the information processing device 10may acquire a similar expression 43 corresponding to the received causalrelationship 420 or the causal relationship 420 identified by the causalrelationship ID from the similar expression acquisition module 22C, andtransmit the similar expression 43 to the terminal device 30. Inaddition, the display control module 22G may transmit the co-occurrenceword 45 corresponding to the similar expression (word in bold) selectedby the user among the similar expressions 43 of the causal relationship421 to the terminal device 30.

Therefore, on the display screen 53 of the terminal device 30, thesimilar expression 43 (similar expressions 43A1 and 43A2) correspondingto the selected causal relationship 420 are displayed in the similarexpression display area 63. In addition, on the display screen 53 of theterminal device 30, the co-occurrence word 45 (co-occurrence word 45A1and co-occurrence word 45B1) of the selected causal relationship 420 isdisplayed in the co-occurrence word display area 66.

In this way, the display control module 22G may display the displayscreen 56 including the similar expression 43 and the co-occurrence word45 of the causal relationship 42 selected by the user on the UI unit 34of the terminal device 30.

Returning to FIG. 2, the description will be continued. The receptionmodule 22H receives the input of the generalized expression 44 of thecausal relationship 42 displayed on the display screen 50. In thepresent embodiment, the reception module 22H receives an input of thegeneralized expression 44 by receiving the generalized expression 44selected or input by the user from the terminal device 30. In thepresent embodiment, as described above, the reception module 22Hreceives the generalized expression 44 and the causal relationship ID ofthe causal relationship 42 that was the processing target when thegeneralized expression 44 was input or selected, from the terminaldevice 30.

Note that the display control module 22G may display the display screen50 on the UI unit 14 of the information processing device 10. In thiscase, the reception module 22H may receive the input of the generalizedexpression 44 from the UI unit 14.

The registration module 22I stores the generalized expression 44received by the reception module 22H in the storage unit 12 inassociation with the causal relationship ID of the causal relationship42 that was the processing target when the generalized expression 44 wasinput or selected.

In the present embodiment, the registration module 22I registers thegeneralized expression 44 by updating the causal relationship managementinformation 12B and the generalized management information 12E.

FIG. 14 is a schematic diagram illustrating an example of a dataconfiguration of the generalized management information 12E. Thegeneralized management information 12E is a database in which thegeneralized ID and the generalized expression 44 of the causalrelationship 42 are associated with each other. Note that a data formatof the generalized management information 12E is not limited to thedatabase.

The generalized ID is identification information of the generalizedexpression 44. The generalized expression 44 is configured by of a setof a generalized expression 44A of the cause 42A and a generalizedexpression 44B of the result 42B.

The registration module 22I registers the generalized expression 44received by the reception module 22H in the generalized managementinformation 12E, and also adds the generalized ID to the generalizedexpression 44 to register the generalized expression 44 in thegeneralized management information 12E. In addition, the registrationmodule 22I associates the generalized ID of the generalized expression44 registered in the generalized management information 12E with thecausal relationship ID of the causal relationship 42 that was theprocessing target when the generalized expression 44 was input orselected, and registers the associated generalized ID and casualrelationship ID in the causal relationship management information 12B(see FIG. 5).

Through the processing, the registration module 22I stores thegeneralized expression 44 in the storage unit 12.

For example, it is assumed that the user uses a specific search query tosearch for the document 40 related to the search query. In this case,the control unit 18 of the information processing device 10 searches forthe causal relationship 42 including the received search query from thecausal relationship management information 12B, and searches for thegeneralized expression 44 corresponding to the generalized ID associatedwith the causal relationship 42 from the generalized managementinformation 12E. Then, the control unit 18 of the information processingdevice 10 searches for the document 40 including the searched causalrelationship 42 and generalized expression 44 from the plurality ofdocuments 40 registered in the document management information 12A.

Therefore, the information processing device 10 can search for the inputsearch query or the document 40 including the generalized expression 44for the search query from the document management information 12A.

Therefore, the information processing device 10 can search for thedocument 40 related to the input search query from the plurality oftypes of documents 40 belonging to a plurality of types of categories,without depending on the category to which the document 40 including thesearch query belongs.

Next, an example of a flow of information processing executed by theinformation processing device 10 will be described.

FIG. 15 is a flowchart illustrating an example of a flow of informationprocessing executed by the information processing device 10 of thepresent embodiment.

The target document acquisition module 22A receives the document ID fromthe terminal device 30 (step S100). The target document acquisitionmodule 22A acquires the document 40 as the target document 41 by readingthe document 40 corresponding to the document ID received in step S100from the document management information 12A (step S102).

Next, the causal relationship acquisition module 22B acquires the causalrelationship 42 corresponding to the document ID received in step S100from the causal relationship management information 12B (step S104).Through the processing of step S104, the causal relationship acquisitionmodule 22B acquires one or a plurality of causal relationships 42included in the target document 41 acquired in step S102. Note that asdescribed above, the causal relationship acquisition module 22B mayacquire the causal relationship 42 selected by the user from theplurality of causal relationships 42 included in the target document 41.

Next, the similar expression acquisition module 22C acquires the similarexpression 43 of the causal relationship 42 acquired in step S104 usingthe feature management information 12C (step S106). Note that asdescribed above, the similar expression acquisition module 22C mayacquire the similar expression 43 using the priority determinationresult of the priority determination module 22E. In addition, asdescribed above, the similar expression acquisition module 22C mayacquire the similar expression 43 of a predetermined part of speech.

Next, the co-occurrence word acquisition module 22F acquires theco-occurrence words of the words that constitute the causal relationship42 included in the target document 41 acquired in step S102 from theco-occurrence management information 12D (step S108). Note that asdescribed above, the processing of step 5108 may be omitted.

Next, the display control module 22G displays the display screen 50 onthe UI unit 34 of the terminal device 30 (step S110). The display screen50 includes the causal relationship 42 acquired in step S104 and thesimilar expression 43 acquired in step S106. In addition, as describedabove, the display screen 50 may further include the co-occurrence words45 acquired in step S108, the target document 41, and the like, inaddition to the causal relationship 42 and the similar expression 43.

The user inputs or selects the generalized expression 44 of the causalrelationship 42 while visually checking the causal relationship 42, thesimilar expression 43, the target document 41, and the co-occurrenceword 45 displayed on the display screen 50. The conversion supportprocessing module 22 repeats a negative determination (step S112: No)until it is determined that the generalized expression 44 is receivedfrom the terminal device 30. When the conversion support processingmodule 22 determines that the generalized expression 44 has beenreceived from the terminal device 30 (step S112: Yes), the processingproceeds to step S114.

When a positive determination is made in step S112 (step S112: Yes), theregistration module 22I receives the generalized expression 44 that hasbeen input or selected, and the causal relationship ID of the causalrelationship 42 that was the processing target when the generalizedexpression 44 was input, from the terminal device 30.

The registration module 22I associates the generalized expression 44received in step S112 with the causal relationship ID of the causalrelationship 42 that was the processing target when the generalizedexpression 44 was input or selected, and resisters the associatedgeneralized expression 44 and causal relationship ID in the causalrelationship management information 12B and the generalized managementinformation 12E (step S114). Then, the present routine ends.

Note that when the causal relationship acquisition module 22B acquires aplurality of causal relationships 42 in step S104, the plurality ofcausal relationships 42 may be displayed on the UI unit 34, and theprocessing of steps S106 to S114 may be executed for the causalrelationship 42 selected by the user.

As described above, the information processing device 10 of the presentembodiment includes the causal relationship acquisition module 22B, thesimilar expression acquisition module 22C, and the acquisition module22D. The causal relationship acquisition module 22B acquires the causalrelationship 42 included in the target document 41 that is the specificdocument 40 from the causal relationship management information 12B. Thecausal relationship management information 12B is registered with one ora plurality of causal relationships 42, which are extracted from one ora plurality of documents 40 and are configured by a set of the firstelement (cause 42A) and the second element (result 42B) having arelationship. The similar expression acquisition module 22C acquires thesimilar expression 43 of the causal relationship 42 included in thetarget document 41 based on the feature management information 12C. Thefeature of each of the plurality of words included in the one or theplurality of documents 40 is registered in the similar expressionacquisition module 22C. The acquisition module 22D acquires thegeneralized expression 44 of the causal relationship 42 included in thetarget document 41 based on the causal relationship 42 included in thetarget document 41 and the similar expression 43.

Here, in the related art, in order to obtain the causal relationship ofgeneralized expressions, it is necessary to prepare an important worddictionary and a generalized dictionary in advance, and maintenance ofthe dictionaries is necessary. In addition, in order to obtain ageneralized expression of the causal relationship included in aplurality of documents in different categories, it was necessary toperform conversion in consideration of the meaning of the causalrelationship with the user, not simple conversion. Therefore, in theconventional technology, it may be difficult to improve a conversionefficiency of the causal relationship included in a target document intothe generalized expression.

On the other hand, the information processing device 10 of the presentembodiment acquires the causal relationship 42 of the target document 41from the causal relationship management information 12B. In addition,the information processing device 10 of the present embodiment acquiresthe similar expression 43 of the causal relationship 42 from the featuremanagement information 12C. Then, the information processing device 10acquires the generalized expression 44 of the causal relationship 42included in the target document 41 using the causal relationship 42 ofthe target document 41 and the similar expression 43 of the causalrelationship 42.

Therefore, in the information processing device 10 of the presentembodiment, a generalized expression 44 that does not depend on thecategory to which the target document 41 belongs can be easily acquiredfor the causal relationship 42 included in the target document 41without using the dictionary such as the important word dictionary orthe generalized dictionary.

Accordingly, in the information processing device 10 of the presentembodiment, it is possible to improve the conversion efficiency of thecausal relationship 42 included in the target document 41 into thegeneralized expression 44.

In addition, in the information processing device 10 of the presentembodiment, the acquisition module 22D includes the display controlmodule 22G, the reception module 22H, and the registration module 22I.The display control module 22G displays the display screen 50 includingthe causal relationship 42 included in the target document 41 and thesimilar expression 43 on the UI unit 34. The reception module 22Hreceives the input of the generalized expression 44 of the causalrelationship 42 displayed on the display screen 50. The registrationmodule 22I registers the received generalized expression 44 as thegeneralized expression 44 of the causal relationship 42.

As described above, the information processing device 10 of the presentembodiment displays the display screen 50 including the causalrelationship 42 included in the target document 41 and the similarexpression 43 of the causal relationship 42 on the UI unit 34.Therefore, the user can easily recall another similar expression byvisually recognizing the causal relationship 42 and the similarexpression 43 and easily input the generalized expression 44.

Therefore, the information processing device 10 of the presentembodiment can effectively support the conversion into the generalizedexpression 44, in addition to the effects described above.

In addition, in the information processing device 10 of the presentembodiment, the acquisition module 22D stores the acquired generalizedexpression 44 in the storage unit 12 in association with the causalrelationship ID of the causal relationship 42 that was the processingtarget when the generalized expression 44 was acquired. For example, theregistration module 22I registers the generalized expression 44 receivedby the reception module 22H in the generalized management information12E, and also adds the generalized ID to the generalized expression 44to register the generalized expression 44 in the generalized managementinformation 12E. In addition, the registration module 22I associates thegeneralized ID of the generalized expression 44 registered in thegeneralized management information 12E with the causal relationship IDof the causal relationship 42 that was the processing target when thegeneralized expression 44 was input or selected, and registers theassociated generalized ID and casual relationship ID in the causalrelationship management information 12B (see FIG. 5).

Therefore, the information processing device 10 can search for thedocument 40 related to the input search query from the plurality oftypes of documents 40 belonging to a plurality of types of categories,without depending on the category to which the document 40 including theinput search query belongs.

Here, it is assumed that the document 40 is case data of a trouble. Inorder to avoid the trouble, it is important for the user to grasp pasttroubles and not to repeat the same trouble. For example, in the case ofproducts used in thermal power, hydraulic power, and nuclear powerplants, building air-conditioning management systems, water and sewermanagement systems, and the like, due to long service life thereof, thenumber of new product designs involving one designer is limited.

For this reason, in the past, regarding the past troubles andcountermeasures thereof, it was largely dependent on the knowledge ofveteran designers. For this reason, in the past, there was a risk thatnecessary knowledge would be lost if it could not be passed on in theform of instruction to young designers.

Therefore, in recent years, case data (documents) such as phenomena,causes, and countermeasures of the troubles that occurred in the pasthave been compiled into a database, and when a new product is designed,a search system that refers to past documents generated by existingsimilar products has been introduced.

However, the conventional search system searches for all the documentsregistered in the database and searches for a document including aninput search query, and thus, a large number of unintended documents maybe searched. Therefore, conventionally, it may take time to select adocument required by the user from a large number of obtained searchresults. In addition, when a young designer with little designexperience searches for the documents using the conventional searchsystem, it may be difficult to determine whether the searched documentis a document related to a current design. In addition, conventionally,since only documents including the input search query are searched,documents including synonymous words having different notations have notbeen searched.

In addition, when the categories are different, different notations maybe given even if the words have similar meanings. Therefore, in therelated art, it may be difficult to search for an intended document evenwhen a synonym dictionary or the like is used.

In addition, as described above, in the related art in which theimportant word dictionary and the generalized dictionary are prepared inadvance, maintenance of the dictionaries is required. In addition, inthe related art, in order to obtain the generalized expression of thecausal relationship included in the plurality of documents in differentcategories, it was necessary to perform conversion in consideration ofthe meaning of the causal relationship with the user, not simpleconversion.

Therefore, in the related art, it was difficult to search for thedocument 40 related to the input search query from the plurality oftypes of documents 40 belonging to a plurality of types of categories,without depending on the category to which the document 40 including theinput search query belongs.

On the other hand, in the information processing device 10 of thepresent embodiment, the acquisition module 22D acquires the generalizedexpression 44 of the causal relationship 42 based on the causalrelationship 42 acquired by the causal relationship acquisition module22B, and the similar expression 43 acquired by the similar expressionacquisition module 22C. Then, the acquisition module 22D stores theacquired generalized expression 44 in the storage unit 12 in associationwith the causal relationship ID of the causal relationship 42 that wasthe processing target when the generalized expression 44 was acquired.

Therefore, the information processing device 10 of the presentembodiment can search for the document 40 related to the input searchquery from the plurality of types of documents 40 belonging to aplurality of types of categories, without depending on the category towhich the document 40 including the search query belongs, in addition tothe effects described above.

For example, it is assumed that the user uses a specific search query tosearch for the document 40 related to the search query. In this case,the control unit 18 of the information processing device 10 searches forthe causal relationship 42 including the received search query from thecausal relationship management information 12B, and searches for thegeneralized expression 44 corresponding to the generalized ID associatedwith the causal relationship 42 from the generalized managementinformation 12E. Then, the control unit 18 of the information processingdevice 10 searches for the document 40 including the searched causalrelationship 42 and generalized expression 44 from the plurality ofdocuments 40 registered in the document management information 12A.

Thus, in the information processing device 10, the cause 42A and theresult 42B included in the causal relationship 42, and the generalizedexpression 44 can be used as the search target of the input searchquery. Therefore, the information processing device 10 can increase apossibility that the document 40 intended by the user is searched. Inaddition, the information processing device 10 of the present embodimentcan also search for the similar expression 43 of the causal relationship42, and thus suppress the document 40 that is not the search targetbecause the expression is different from the search query.

MODIFICATION

In the embodiment, the form in which the acquisition module 22D providesthe user with the display screen 50 including at least the causalrelationship 42 and the similar expression 43, and receives thegeneralized expression 44 input or selected by the user to acquire thegeneralized expression 44 has been described as an example.

However, the acquisition module 22D may be configured to acquire thegeneralized expression 44 based on the causal relationship 42 of thetarget document 41 and the similar expression 43 of the causalrelationship 42 without the input of the user.

FIG. 16 is a block diagram illustrating an example of a functionalconfiguration of an information processing device 11 of the presentmodification. Note that parts having the same functions andconfigurations as those of the information processing device 10 of theembodiment described above are given the same reference numerals, anddetailed description thereof will be omitted.

The information processing device 11 includes a storage unit 13, a UIunit 14, a communication unit 16, and a control unit 17. The storageunit 13, the UI unit 14, the communication unit 16, and the control unit17 are connected via a bus 19 so that data or signals can be exchanged.

The storage unit 13 stores document management information 12A, causalrelationship management information 12B, feature management information12C, co-occurrence management information 12D, generalized managementinformation 12E, and a learning model 12F. That is, the storage unit 13is the same as the storage unit 12 of the embodiment described aboveexcept that the learning model 12F is further stored.

The learning model 12F is a model for acquiring the generalizedexpression 44 of the causal relationship 42 from the causal relationship42 included in the target document 41 and the similar expression 43. Thelearning model 12F is learned by a model learning module 20C describedlater.

The control unit 17 includes a learning module 21 and a conversionsupport processing module 23. The learning module 21 includes a causalrelationship learning module 20A, a feature learning module 20B, and amodel learning module 21C. The learning module 21 is the same as thelearning module 20 of the embodiment described above except that themodel learning module 21C is further included.

The model learning module 21C learns the learning model 12F. Forexample, the learning module 21F uses a set of the causal relationship42 and the generalized expression 44 as learning data, and learns amodel for acquiring the generalized expression 44 of the causalrelationship 42 from the learning data by using known machine learning.Note that the model learning module 21C may use the causal relationship42, the similar expression 43, the co-occurrence word 45 of the causalrelationship 42, and the generalized expression 44 that is a correctanswer, as the learning data.

The conversion support processing module 23 includes a target documentacquisition module 22A, a causal relationship acquisition module 22B, asimilar expression acquisition module 22C, and an acquisition module23D. The conversion support processing module 23 is the same as theconversion support processing module 22 of the embodiment describedabove except that the acquisition module 22D is replaced with theacquisition module 23D.

The acquisition module 23D inputs the causal relationship 42 acquired bythe causal relationship acquisition module 22B and the similarexpression 43 acquired by the similar expression acquisition module 22Cto the learning model 12F as input data. By the processing, theacquisition module 23D acquires the generalized expression 44 as outputdata from the learning model 12F.

Note that the acquisition module 23D may be configured to include theco-occurrence word acquisition module 22F, similarly to the acquisitionmodule 22D of the information processing device 10 of the embodimentdescribed above. In this case, the acquisition module 23D may input thecausal relationship 42 acquired by the causal relationship acquisitionmodule 22B, the similar expression 43 acquired by the similar expressionacquisition module 22C, and the co-occurrence word 45 acquired by theco-occurrence word acquisition module 22F to the learning model 12F asinput data. By the processing, the acquisition module 23D acquires thegeneralized expression 44 as output data from the learning model 12F.

As described above, the acquisition module 23D may acquire thegeneralized expression 44 of the causal relationship 42 from the causalrelationship 42 included in the target document 41 and the similarexpression 43 of the causal relationship 42 by using the learning model12F.

Next, an example of the hardware configurations of the informationprocessing device 10, the information processing device 11, and theterminal device 30 in the embodiment and modification described abovewill be described.

FIG. 17 is an example of a hardware configuration diagram of theinformation processing device 10, the information processing device 11,and the terminal device 30 according to the embodiment and modificationdescribed above.

The information processing device 10, the information processing device11, and the terminal device 30 include a control device such as a CPU86, a storage device such as a read only memory (ROM) 88 or a randomaccess memory (RAM) 90 or a hard disk drive (HDD) 92, an I/F unit 82that is an interface with various devices, an output unit 80 thatoutputs various types of information such as output information, aninput unit 94 that receives user operations, and a bus 96 that connectsthe respective units, and have a hardware configuration using a normalcomputer.

In the information processing device 10, the information processingdevice 11, and the terminal device 30, the CPU 86 reads out a programfrom the ROM 88 onto the RAM 90 and executes the program to implementthe respective modules on the computer.

Note that the program for executing each processing executed by theinformation processing device 10, the information processing device 11,and the terminal device 30 may be stored in the HDD 92. In addition, aprogram for executing each processing executed by the informationprocessing device 10, the information processing device 11, and theterminal device 30 may be provided by being incorporated in the ROM 88in advance.

In addition, the program for executing the processing executed by theinformation processing device 10, the information processing device 11,and the terminal device 30 may be provided as a computer program productthat is stored in a computer-readable storage medium such as a CD-ROM, aCD-R, a memory card, a digital versatile disk (DVD), or a flexible disk(FD) in a file in an installable format or an executable format. Inaddition, the program for executing the processing executed by theinformation processing device 10, the information processing device 11,and the terminal device 30 may be stored on a computer connected to anetwork such as the Internet, and provided by being downloaded via thenetwork. In addition, the program for executing the processing executedby the information processing device 10, the information processingdevice 11, and the terminal device 30 may be provided or distributed viathe network such as the Internet.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An information processing device comprising: oneor more hardware processors configured to: acquire a causal relationshipincluded in a target document that is a specific document from causalrelationship management information in which one or a plurality ofcausal relationships are registered, the one or the plurality of causalrelationships being extracted from one or a plurality of documents andeach including a set of a first element and a second element having arelationship; acquire a similar expression of the causal relationshipincluded in the target document, based on feature management informationin which features of a plurality of words included in the one or theplurality of document are registered; and acquire a generalizedexpression of the causal relationship included in the target document,based on the causal relationship included in the target document and thesimilar expression.
 2. The device according to claim 1, wherein thehardware processor is configured to: display a display screen includingthe causal relationship included in the target document and the similarexpression on a display unit; receive an input of the generalizedexpression of the causal relationship displayed on the display screen;and register the received generalized expression as the generalizedexpression of the causal relationship.
 3. The device according to claim2, wherein the hardware processor is configured to: display the displayscreen including the causal relationship included in the targetdocument, the similar expression, and a selection screen of candidatesof the generalized expression of the causal relationship on the displayunit; and receive an input of the generalized expression selected fromthe candidates of the selection screen.
 4. The device according to claim1, wherein the hardware processor is configured to acquire thegeneralized expression of the causal relationship using a learning modelfor acquiring the generalized expression of the causal relationship fromthe causal relationship included in the target document and the similarexpression.
 5. The device according to claim 1, wherein the hardwareprocessor is configured to acquire a group of words having a featuresimilar to each of words that constitute the causal relationship in thetarget document among the words registered in the feature managementinformation, as the similar expression of the causal relation.
 6. Thedevice according to claim 1, wherein the hardware processor isconfigured to: determine a priority of each of the words registered inthe feature management information; and acquire a group of words havinga priority higher than a predetermined first priority among other wordshaving the feature similar to each of the words that constitute thecausal relationship in the target document, as the similar expression ofthe causal relationship.
 7. The device according to claim 6, wherein thehardware processor is configured to specify a category to which thetarget document belongs, and determine a higher priority as more wordsare included in the documents belonging to other categories other thanthe specified category.
 8. The device according to claim 1, wherein thehardware processor is configured to acquire a group of words in whichthe feature is similar to each of words of a predetermined part ofspeech that constitute the causal relationship in the target documentamong the words registered in the feature management information, as thesimilar expression of the causal relationship.
 9. The device accordingto claim 1, wherein the hardware processor is configured to: acquireco-occurrence words related to the words that constitute the causalrelationship included in the target document; and acquire thegeneralized expression based on the causal relationship included in thetarget document, the similar expression, and the co-occurrence words.10. The device according to claim 9, wherein the hardware processor isconfigured to acquire a co-occurrence word corresponding to the word forwhich selection is received, among the co-occurrence words related tothe words that constitute the causal relationship included in the targetdocument in co-occurrence management information in which theco-occurrence words of the plurality of words are registered.
 11. Thedevice according to claim 1, wherein the hardware processor isconfigured to acquire the selected causal relationship among theplurality of causal relationships acquired from the causal relationshipmanagement information.
 12. An information processing system comprising:an information processing device; and a terminal device thatcommunicates with the information processing device, the informationprocessing device comprising: one or more hardware processors configuredto: acquire a causal relationship included in a target document that isa specific document from causal relationship management information inwhich one or a plurality of causal relationships are registered, the oneor a plurality of causal relationships being extracted from one or aplurality of documents and each including a set of a first element and asecond element having a relationship; acquire a similar expression ofthe causal relationship included in the target document, based onfeature management information in which features of a plurality of wordsincluded in the one or the plurality of document are registered; acquirea generalized expression of the causal relationship included in thetarget document, based on the causal relationship included in the targetdocument and the similar expression; display a display screen includingthe causal relationship included in the target document and the similarexpression on a display unit of the terminal device; receive an input ofthe generalized expression of the causal relationship displayed on thedisplay screen; and register the received generalized expression as thegeneralized expression of the causal relationship.
 13. A computerprogram product comprising a non-transitory computer-readable mediumincluding programmed instructions, the instructions causing a computerto execute: acquiring a causal relationship included in a targetdocument that is a specific document from causal relationship managementinformation in which one or a plurality of causal relationships areregistered, the one or a plurality of causal relationships beingextracted from one or a plurality of documents and each including a setof a first element and a second element having a relationship; acquiringa similar expression of the causal relationship included in the targetdocument, based on feature management information in which features of aplurality of words included in the one or the plurality of document areregistered; and acquiring a generalized expression of the causalrelationship included in the target document, based on the causalrelationship included in the target document and the similar expression.